Sparsh Pratik
National Chiao Tung University
9 Papers
14 Citations
Sparsh Pratik is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Computer science & Resistive random-access memory. The author has an hindex of 2, co-authored 3 publications.
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Papers
Fully Photon Controlled Synaptic Memristor for Neuro‐Inspired Computing
TL;DR: In this paper , a light stimulated synaptic memristor (LSSM) based on ZnO/Zn2SnO4 heterostructure is prepared with the characteristics of reversibly tunable conductance states by varying the wavelength of the incident light.
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A Process-Aware Memory Compact-Device Model Using Long-Short Term Memory
Albert Lin,Sparsh Pratik,Jun Ota,Tejender Singh Rawat,Tzu-Hsiang Huang,C. H. Hsu,Wei-Ming Su,Tseung-Yuen Tseng +7 more
TL;DR: In this article, the authors proposed a unified, general-purpose, process-aware machine learning (ML) based compact model (CM) for resistive random-access memory (RRAM), and the same methodology can be used for any memory devices with hysteresis.
RRAM Compact Modeling Using Physics and Machine Learning Hybridization
Albert S. Lin,Po-Ning Liu,Sparsh Pratik,Zheng-Kai Yang,Tejender Singh Rawat,Tseung-Yuen Tseng +5 more
TL;DR: The results show that the physics-assisted architecture enables simpler ML models in reference to the previous work of long short-term memory (LSTM)-based RRAM CMs, and suggest that the uniform framework with hybridization in physics and ML should be the most efficient way in future compact device modeling.
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Intelligent Photolithography Corrections Using Dimensionality Reductions
Parag Parashar,Chandni Akbar,Tejender Singh Rawat,Sparsh Pratik,Rajat Butola,Shih H. Chen,Yung Sung Chang,Sirapop Nuannimnoi,Albert Lin +8 more
TL;DR: This work uses dimensionality reduction (DR) algorithms to reduce the computation time of complex OPC/EPC problems while the prediction accuracy is maintained, and implements a pure machine learning approach where the input masks are directly mapped to the output etched patterns.
ZTO/MgO-Based Optoelectronic Synaptic Memristor for Neuromorphic Computing
TL;DR: In this article, the authors improved the resistive switching and synaptic characteristics of a Zn2SnO4 (ZTO)-based optoelectronic synaptic memristor (OSM) by the insertion of an ultrathin MgO layer.
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